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Dissertation Genome-Scale Metabolic Modeling Of DISSERTATION GENOME-SCALE METABOLIC MODELING OF CYANOBACTERIA: NETWORK STRUCTURE, INTERACTIONS, RECONSTRUCTION AND DYNAMICS Submitted by Chintan Jagdishchandra Joshi Department of Chemical and Biological Engineering In partial fulfilment of the requirements For the Degree of Doctor of Philosophy Colorado State University Fort Collins, Colorado Fall 2016 Doctoral Committee: Advisor: Ashok Prasad Christie A. M. Peebles Kenneth Reardon Graham Peers Copyright by Chintan Jagdishchandra Joshi 2016 All Rights Reserved ABSTRACT GENOME-SCALE METABOLIC MODELING OF CYANOBACTERIA: NETWORK STRUCTURE, INTERACTIONS, RECONSTRUCTION AND DYNAMICS Metabolic network modeling, a field of systems biology and bioengineering, enhances the quantitative predictive understanding of cellular metabolism and thereby assists in the development of model-guided metabolic engineering strategies. Metabolic models use genome- scale network reconstructions, and combine it with mathematical methods for quantitative prediction. Metabolic system reconstructions, contain information on genes, enzymes, reactions, and metabolites, and are converted into two types of networks: (i) gene-enzyme-reaction, and (ii) reaction-metabolite. The former details the links between the genes that are known to code for metabolic enzymes, and the reaction pathways that the enzymes participate in. The latter details the chemical transformation of metabolites, step by step, into biomass and energy. The latter network is transformed into a system of equations and simulated using different methods. Prominent among these are constraint-based methods, especially Flux Balance Analysis, which utilizes linear programming tools to predict intracellular fluxes of single cells. Over the past 25 years, metabolic network modeling has had a range of applications in the fields of model-driven discovery, prediction of cellular phenotypes, analysis of biological network properties, multi- species interactions, engineering of microbes for product synthesis, and studying evolutionary processes. This thesis is concerned with the development and application of metabolic network modeling to cyanobacteria as well as E. coli. ii Chapter 1 is a brief survey of the past, present, and future of constraint-based modeling using flux balance analysis in systems biology. It includes discussion of (i) formulation, (ii) assumption, (iii) variety, (iv) availability, and (v) future directions in the field of constraint based modeling. Chapter 2, explores the enzyme-reaction networks of metabolic reconstructions belonging to various organisms; and finds that the distribution of the number of reactions an enzyme participates in, i.e. the enzyme-reaction distribution, is surprisingly similar. The role of this distribution in the robustness of the organism is also explored. Chapter 3, applies flux balance analysis on models of E. coli, Synechocystis sp. PCC6803, and C. reinhardtii to understand epistatic interactions between metabolic genes and pathways. We show that epistatic interactions are dependent on the environmental conditions, i.e. carbon source, carbon/oxygen ratio in E. coli, and light intensity in Synechocystis sp. PCC6803 and C. reinhardtii. Cyanobacteria are photosynthetic organisms and have great potential for metabolic engineering to produce commercially important chemicals such as biofuels, pharmaceuticals, and nutraceuticals. Chapter 4 presents our new genome scale reconstruction of the model cyanobacterium, Synechocystis sp. PCC6803, called iCJ816. This reconstruction was analyzed and compared to experimental studies, and used for predicting the capacity of the organism for (i) carbon dioxide remediation, and (ii) production of intracellular chemical species. Chapter 5 uses our new model iCJ816 for dynamic analysis under diurnal growth simulations. We discuss predictions of different optimization schemes, and present a scheme that qualitatively matches observations. iii ACKNOWLEDGEMENTS I started my journey in the field of metabolic modeling about seven years ago, while I was a Professional Science Masters’ (PSM) student at Oregon State University. Little did I know that modeling of MAPK pathway in bioprocess control systems, a class taught by Dr. Ganti Murthy, will send me down a path to pursue doctorate, an year later. Now, after 6 years as I come to the final steps of my doctorate, I want to take an opportunity to express my gratitude to my advisor, co-advisor, committee members, professors, colleagues, department secretaries, friends, and family. These 6 years would not have been as productive, if it was not for these thoughtful, brilliant, dedicated, and hardworking people. I have truly come to understand the meaning of the phrase, “It takes a village…” I deeply express my heartfelt gratitude to my advisor, Dr. Ashok Prasad, for his consistent faith in me since the inception of our student-advisor relationship. I highly commend his scientific enthusiasm, to allow me free rein at the choice of projects, which a young student can only dream about. Though he challenged me to do my best work, he also made sure that I remain on track rather than pursue tangents. I find myself indebted to his insurmountable patience, and support, during these 6 years of my learning in matters both professional and personal. I am grateful to my co-advisor, Dr. Christie Peebles, who enhanced my learning with regular discussions on experimental biology of E. coli and cyanobacteria. Our collaborations on topics (included in this thesis) has greatly helped me in thinking about my work from various different aspects. I found my interactions with her during group and personal meetings as very enlightening. iv I would also like to thank my committee members Dr. Graham Peers and Dr. Kenneth Reardon. Dr. Graham Peers has pushed my understanding of cyanobacterial photosynthesis, and challenged me to think at the interface of computational and experimental biology. Dr. Kenneth Reardon has been of tremendous help in my learning of presenting scientific research, be it for a conference or in-class projects. His experience with scientific research, in academia and industry alike, provides an inspiration to aspiring scientists like me. I would also like to acknowledge all the professors in the Department of Chemical and Biological Engineering who played a crucial role in establishing my fundamentals of not only chemical engineering, but also of scientific research itself. Sincere thanks are in order to my colleague Katherine Schaumberg, Wenlong Xu, Elaheh Alizade, Forrest Estep, Yi Ern Cheah, and Allison Zimont for helping me flesh out my manuscripts; and lending an ear for discussions ranging from experiments in cyanobacteria to systems biology. I would also like to extend my thanks to all other past and present members of Prasad lab and Peebles lab. I would also like to thank Mary Tracey, an undergraduate student from Peebles lab, who helped in the literature survey of cyanobacterial genes; and Aidan Ceney, a joint REU student from Peebles and Prasad lab, for taking initiative for future work in thermodynamic calculations of cyanobacterial metabolic network. Faculty and staff at Department of Chemical and Biological Engineering (CBE) and Scott Engineering building including Claire Laville, Denise Morgan, and Marilyn Gross have had a special role for their support through my 6 years at Colorado State University. A special note goes to my friends in Fort Collins, who were always there for support during this whole journey. v Lastly, and most importantly, I am grateful to my parents and my brother for their patience, encouragement, and love which gives me strength to do better work each day. Their support has made me who I am. vi DEDICATION To my parents and brother vii TABLE OF CONTENTS ABSTRACT .................................................................................................................................... ii ACKNOWLEDGEMENTS ........................................................................................................... iv DEDICATION .............................................................................................................................. vii LIST OF TABLES ........................................................................................................................ xii LIST OF FIGURES ..................................................................................................................... xiii CHAPTER 1. ............ CONSTRAINT BASED MODELING OF METABOLIC NETWORKS IN SYSTEMS BIOLOGY.................................................................................................................... 1 1. SYSTEMS BIOLOGY......................................................................................................... 1 1.1. PARTS .......................................................................................................................... 1 1.2. SUM OF ITS PARTS ................................................................................................... 2 1.3. THE WHOLE ............................................................................................................... 3 2. METABOLIC MODELING LANDSCAPE ....................................................................... 5 2.1. METABOLIC NETWORK RECONSTRUCTIONS ................................................... 8 2.2. MATHEMATICAL MODEL ....................................................................................... 8 3. CONSTRAINT BASED MODELING ...............................................................................
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